Abstract
The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step description of the quantitative tests’ execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs’ contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task‐specific accuracy quantification should be expected from the vendors.
Highlights
While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience
We illustrate our approach by applying the tests suggested in the Report to a commercial image registration software package that may have been less explored in the radiotherapy literature in comparison with others
The CT to CT registration had all target registration errors (TRE) below 1⁄2 of the corresponding voxel size
Summary
Image registration is currently widely used in radiation oncology clinical practice. It is a complex subject, and image registration software, such as treatment planning and other radiotherapy software, has to undergo acceptance testing and validation to assess its performance and limitations prior to clinical use. The supplemental materials in the Report contain a series of publicly available image datasets designed to help in quantitating image registration accuracy. We endeavored to provide a detailed step-by-step description of the quantitative tests’ (Section 4.C of the Report) execution, striving for simplicity and utilization of software either in the public domain, or ubiquitous in general (e.g., Microsoft Excel) or in radiotherapy (e.g., a treatment planning system). We illustrate our approach by applying the tests suggested in the Report to a commercial image registration software package that may have been less explored in the radiotherapy literature in comparison with others
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